yuanjs@qutke.com
2 months ago
commit
19f147a6e0
32 changed files with 1376 additions and 0 deletions
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ReadMe.md |
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target/ |
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!.mvn/wrapper/maven-wrapper.jar |
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!**/src/main/**/target/ |
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!**/src/test/**/target/ |
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### STS ### |
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.apt_generated |
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.classpath |
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.factorypath |
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.project |
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.settings |
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.springBeans |
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.sts4-cache |
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### IntelliJ IDEA ### |
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.idea |
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*.iws |
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*.iml |
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*.ipr |
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### NetBeans ### |
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/nbproject/private/ |
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/nbbuild/ |
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/dist/ |
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/nbdist/ |
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/.nb-gradle/ |
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build/ |
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!**/src/main/**/build/ |
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!**/src/test/**/build/ |
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### VS Code ### |
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.vscode/ |
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<?xml version="1.0" encoding="UTF-8"?> |
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<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" |
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xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 https://maven.apache.org/xsd/maven-4.0.0.xsd"> |
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<modelVersion>4.0.0</modelVersion> |
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<parent> |
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<groupId>org.springframework.boot</groupId> |
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<artifactId>spring-boot-starter-parent</artifactId> |
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<version>3.2.6</version> |
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<relativePath/> <!-- lookup parent from repository --> |
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</parent> |
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<groupId>cn.ai</groupId> |
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<artifactId>Spring-Ai-Gu</artifactId> |
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<version>0.0.1-SNAPSHOT</version> |
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<name>Spring-Ai-Gu</name> |
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<description>Spring-Ai-Gu</description> |
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<properties> |
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<maven.compiler.source>17</maven.compiler.source> |
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<maven.compiler.target>17</maven.compiler.target> |
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<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding> |
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<spring-boot.version>3.2.6</spring-boot.version> |
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<knife4j.version>4.3.0</knife4j.version> |
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<langchain4j.version>1.0.0-beta3</langchain4j.version> |
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<mybatis-plus.version>3.5.11</mybatis-plus.version> |
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</properties> |
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|
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<dependencies> |
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<!-- web应用程序核心依赖 --> |
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<dependency> |
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<groupId>org.springframework.boot</groupId> |
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<artifactId>spring-boot-starter-web</artifactId> |
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</dependency> |
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<!-- 编写和运行测试用例 --> |
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<dependency> |
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<groupId>org.springframework.boot</groupId> |
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<artifactId>spring-boot-starter-test</artifactId> |
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<scope>test</scope> |
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</dependency> |
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<!-- 前后端分离中的后端接口测试工具 --> |
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<dependency> |
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<groupId>com.github.xiaoymin</groupId> |
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<artifactId>knife4j-openapi3-jakarta-spring-boot-starter</artifactId> |
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<version>${knife4j.version}</version> |
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</dependency> |
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<!-- 基于open-ai的langchain4j接口：ChatGPT、deepseek都是open-ai标准下的大模型 --> |
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<!-- <dependency>--> |
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<!-- <groupId>dev.langchain4j</groupId>--> |
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<!-- <artifactId>langchain4j-open-ai</artifactId>--> |
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<!-- </dependency>--> |
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<!-- spring整合openai--> |
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<!-- <dependency>--> |
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<!-- <groupId>dev.langchain4j</groupId>--> |
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<!-- <artifactId>langchain4j-open-ai-spring-boot-starter</artifactId>--> |
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<!-- </dependency>--> |
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<!-- 接入阿里云百炼平台 --> |
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<dependency> |
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<groupId>dev.langchain4j</groupId> |
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<artifactId>langchain4j-community-dashscope-spring-boot-starter</artifactId> |
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</dependency> |
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<!--langchain4j高级功能--> |
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<dependency> |
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<groupId>dev.langchain4j</groupId> |
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<artifactId>langchain4j-spring-boot-starter</artifactId> |
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</dependency> |
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<!-- Spring Boot Starter Data mongodb --> |
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<dependency> |
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<groupId>org.springframework.boot</groupId> |
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<artifactId>spring-boot-starter-data-mongodb</artifactId> |
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</dependency> |
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<!-- Mysql Connector --> |
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<dependency> |
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<groupId>com.mysql</groupId> |
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<artifactId>mysql-connector-j</artifactId> |
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</dependency> |
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<!--mybatis-plus 持久层--> |
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<dependency> |
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<groupId>com.baomidou</groupId> |
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<artifactId>mybatis-plus-spring-boot3-starter</artifactId> |
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<version>${mybatis-plus.version}</version> |
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</dependency> |
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<!--解析pdf文档--> |
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<dependency> |
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<groupId>dev.langchain4j</groupId> |
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<artifactId>langchain4j-document-parser-apache-pdfbox</artifactId> |
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</dependency> |
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<!--简单的rag实现--> |
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<dependency> |
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<groupId>dev.langchain4j</groupId> |
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<artifactId>langchain4j-easy-rag</artifactId> |
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</dependency> |
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<!--向量存储 --> |
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<dependency> |
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<groupId>dev.langchain4j</groupId> |
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<artifactId>langchain4j-pinecone</artifactId> |
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</dependency> |
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<!--流式输出--> |
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<dependency> |
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<groupId>org.springframework.boot</groupId> |
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<artifactId>spring-boot-starter-webflux</artifactId> |
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</dependency> |
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<dependency> |
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<groupId>dev.langchain4j</groupId> |
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<artifactId>langchain4j-reactor</artifactId> |
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</dependency> |
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</dependencies> |
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<dependencyManagement> |
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<dependencies> |
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<!--引入SpringBoot依赖管理清单--> |
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<dependency> |
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<groupId>org.springframework.boot</groupId> |
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<artifactId>spring-boot-dependencies</artifactId> |
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<version>${spring-boot.version}</version> |
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<type>pom</type> |
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<scope>import</scope> |
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</dependency> |
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<!--引入langchain4j依赖管理清单--> |
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<dependency> |
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<groupId>dev.langchain4j</groupId> |
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<artifactId>langchain4j-bom</artifactId> |
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<version>${langchain4j.version}</version> |
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<type>pom</type> |
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<scope>import</scope> |
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</dependency> |
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<!--引入百炼依赖管理清单--> |
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<dependency> |
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<groupId>dev.langchain4j</groupId> |
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<artifactId>langchain4j-community-bom</artifactId> |
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<version>${langchain4j.version}</version> |
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<type>pom</type> |
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<scope>import</scope> |
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</dependency> |
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</dependencies> |
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</dependencyManagement> |
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<build> |
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<plugins> |
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<plugin> |
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<groupId>org.springframework.boot</groupId> |
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<artifactId>spring-boot-maven-plugin</artifactId> |
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</plugin> |
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</plugins> |
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</build> |
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</project> |
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package cn.ai.springaigu; |
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import org.springframework.boot.SpringApplication; |
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import org.springframework.boot.autoconfigure.SpringBootApplication; |
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@SpringBootApplication |
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public class SpringAiGuApplication { |
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public static void main(String[] args) { |
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SpringApplication.run(SpringAiGuApplication.class, args); |
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} |
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} |
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package cn.ai.springaigu.asistant; |
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import dev.langchain4j.service.spring.AiService; |
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import static dev.langchain4j.service.spring.AiServiceWiringMode.EXPLICIT; |
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/** |
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* 创建接 |
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*/ |
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//因为我们在配置文件中同时配置了多个大语言模型，所以需要在这里明确指定（EXPLICIT）模型的beanName（qwenChatModel）
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@AiService(wiringMode = EXPLICIT, chatModel = "qwenChatModel") |
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public interface Assistant { |
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String chat(String userMessage); |
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} |
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package cn.ai.springaigu.asistant; |
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import dev.langchain4j.service.UserMessage; |
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import dev.langchain4j.service.spring.AiService; |
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import static dev.langchain4j.service.spring.AiServiceWiringMode.EXPLICIT; |
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// 使用AIService实现聊天记忆
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@AiService( |
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wiringMode = EXPLICIT, |
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chatModel = "qwenChatModel", |
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chatMemory = "chatMemory" |
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) |
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public interface MemoryChatAssistant { |
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@UserMessage("你是我的好朋友，请用上海话回答问题，并且添加一些表情符号。 {{it}}") //{{it}}表示这里唯一的参数的占位符
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String chat(String message); |
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} |
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package cn.ai.springaigu.asistant; |
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import dev.langchain4j.service.MemoryId; |
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import dev.langchain4j.service.SystemMessage; |
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import dev.langchain4j.service.UserMessage; |
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import dev.langchain4j.service.V; |
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import dev.langchain4j.service.spring.AiService; |
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import static dev.langchain4j.service.spring.AiServiceWiringMode.EXPLICIT; |
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// 创建记忆隔离对话智能体
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@AiService( |
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wiringMode = EXPLICIT, |
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chatModel = "qwenChatModel", |
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chatMemory = "chatMemory", |
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chatMemoryProvider = "chatMemoryProvider", |
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tools = "calculatorTools" //配置tools
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) |
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public interface SeparateChatAssistant { |
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/** |
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* 分离聊天记录 |
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* |
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* @param memoryId 聊天id |
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* @param userMessage 用户消息 |
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*/ |
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// @SystemMessage("你是我的好朋友，请用东北话回答问题。")//系统消息提示词
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//@SystemMessage("你是我的好朋友，请用东北话回答问题。今天是{{current_date}}")//系统消息提示词,在提示词中添加当前日期的占位符{{current_date}}
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@SystemMessage(fromResource = "my-prompt-template.txt") |
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String chat(@MemoryId int memoryId, @UserMessage String userMessage); |
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@UserMessage("你是我的好朋友，请用粤语回答问题。{{message}}") |
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String chat2(@MemoryId int memoryId, @V("message") String userMessage); |
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@SystemMessage(fromResource = "my-prompt-template3.txt") |
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String chat3( |
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@MemoryId int memoryId, |
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@UserMessage String userMessage, |
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@V("username") String username, |
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@V("age") int age |
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); |
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} |
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package cn.ai.springaigu.asistant; |
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import dev.langchain4j.service.*; |
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import dev.langchain4j.service.spring.AiService; |
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import reactor.core.publisher.Flux; |
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import static dev.langchain4j.service.spring.AiServiceWiringMode.EXPLICIT; |
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@AiService( |
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wiringMode = EXPLICIT, |
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//chatModel = "qwenChatModel",
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streamingChatModel = "qwenStreamingChatModel", |
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chatMemoryProvider = "chatMemoryProviderXiaozhi", |
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tools = "appointmentTools", //tools配置
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contentRetriever = "contentRetrieverXiaozhiPincone") //配置向量存储
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public interface XiaozhiAgent { |
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@SystemMessage(fromResource = "zhaozhi-prompt-template.txt") |
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Flux<String> chat(@MemoryId Long memoryId, @UserMessage String userMessage); |
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} |
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package cn.ai.springaigu.config; |
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import dev.langchain4j.data.segment.TextSegment; |
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import dev.langchain4j.model.embedding.EmbeddingModel; |
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import dev.langchain4j.store.embedding.EmbeddingStore; |
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import dev.langchain4j.store.embedding.pinecone.PineconeEmbeddingStore; |
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import dev.langchain4j.store.embedding.pinecone.PineconeServerlessIndexConfig; |
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import org.springframework.beans.factory.annotation.Autowired; |
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import org.springframework.beans.factory.annotation.Value; |
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import org.springframework.context.annotation.Bean; |
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import org.springframework.context.annotation.Configuration; |
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@Configuration |
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public class EmbeddingStoreConfig { |
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@Value("${pinecone.api-key}") |
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private String apiKey; |
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@Autowired |
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private EmbeddingModel embeddingModel; |
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@Bean |
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public EmbeddingStore<TextSegment> embeddingStore() { |
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//创建向量存储
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EmbeddingStore<TextSegment> embeddingStore = PineconeEmbeddingStore.builder() |
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.apiKey(apiKey) |
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.index("ai-study-index")//如果指定的索引不存在，将创建一个新的索引
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.nameSpace("ai-study-namespace") //如果指定的名称空间不存在，将创建一个新的名称 空间
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.createIndex(PineconeServerlessIndexConfig.builder() |
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.cloud("AWS") //指定索引部署在 AWS 云服务上。
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.region("us-east-1") //指定索引所在的 AWS 区域为 us-east-1。
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.dimension(embeddingModel.dimension()) //指定索引的向量维度，该维度与 embeddedModel 生成的向量维度相同。
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.build()) |
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.build(); |
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return embeddingStore; |
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} |
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} |
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package cn.ai.springaigu.config; |
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import dev.langchain4j.memory.ChatMemory; |
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import dev.langchain4j.memory.chat.MessageWindowChatMemory; |
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import org.springframework.context.annotation.Bean; |
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import org.springframework.context.annotation.Configuration; |
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// 配置ChatMemory
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@Configuration |
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public class MemoryChatAssistantConfig { |
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@Bean |
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ChatMemory chatMemory() { |
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//设置聊天记忆记录的message数量
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return MessageWindowChatMemory.withMaxMessages(10); |
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} |
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} |
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package cn.ai.springaigu.config; |
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import cn.ai.springaigu.store.MongoChatMemoryStore; |
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import dev.langchain4j.memory.chat.ChatMemoryProvider; |
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import dev.langchain4j.memory.chat.MessageWindowChatMemory; |
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import org.springframework.beans.factory.annotation.Autowired; |
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import org.springframework.context.annotation.Bean; |
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import org.springframework.context.annotation.Configuration; |
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@Configuration |
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public class SeparateChatAssistantConfig { |
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@Autowired |
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private MongoChatMemoryStore mongoChatMemoryStore; |
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@Bean |
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ChatMemoryProvider chatMemoryProvider() { |
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return memoryId -> MessageWindowChatMemory.builder() |
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.id(memoryId) |
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.maxMessages(10) |
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.chatMemoryStore(mongoChatMemoryStore)//配置持久化对象
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.build(); |
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} |
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} |
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package cn.ai.springaigu.config; |
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import cn.ai.springaigu.store.MongoChatMemoryStore; |
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import dev.langchain4j.data.document.Document; |
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import dev.langchain4j.data.document.loader.FileSystemDocumentLoader; |
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import dev.langchain4j.data.segment.TextSegment; |
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import dev.langchain4j.memory.chat.ChatMemoryProvider; |
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import dev.langchain4j.memory.chat.MessageWindowChatMemory; |
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import dev.langchain4j.model.embedding.EmbeddingModel; |
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import dev.langchain4j.rag.content.retriever.ContentRetriever; |
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import dev.langchain4j.rag.content.retriever.EmbeddingStoreContentRetriever; |
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import dev.langchain4j.store.embedding.EmbeddingStore; |
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import dev.langchain4j.store.embedding.EmbeddingStoreIngestor; |
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import dev.langchain4j.store.embedding.inmemory.InMemoryEmbeddingStore; |
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import org.springframework.beans.factory.annotation.Autowired; |
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import org.springframework.context.annotation.Bean; |
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import org.springframework.context.annotation.Configuration; |
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import java.util.Arrays; |
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import java.util.List; |
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@Configuration |
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public class XiaozhiAgentConfig { |
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@Autowired |
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private MongoChatMemoryStore mongoChatMemoryStore; |
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@Bean |
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ChatMemoryProvider chatMemoryProviderXiaozhi() { |
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return memoryId -> MessageWindowChatMemory.builder() |
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.id(memoryId) |
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.maxMessages(20) |
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.chatMemoryStore(mongoChatMemoryStore) |
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.build(); |
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} |
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@Bean |
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ContentRetriever contentRetrieverXiaozhi() { |
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//使用FileSystemDocumentLoader读取指定目录下的知识库文档
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//并使用默认的文档解析器对文档进行解析
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||||
|
Document document1 = FileSystemDocumentLoader.loadDocument("D:/学习/课件/knowledge/医院信息.md"); |
||||
|
Document document2 = FileSystemDocumentLoader.loadDocument("D:/学习/课件/knowledge/科室信息.md"); |
||||
|
Document document3 = FileSystemDocumentLoader.loadDocument("D:/学习/课件/knowledge/神经内科.md"); |
||||
|
List<Document> documents = Arrays.asList(document1, document2, document3); |
||||
|
//使用内存向量存储 生产不建议使用
|
||||
|
InMemoryEmbeddingStore<TextSegment> embeddingStore = new InMemoryEmbeddingStore<>(); |
||||
|
//使用默认的文档分割器
|
||||
|
EmbeddingStoreIngestor.ingest(documents, embeddingStore); |
||||
|
//从嵌入存储（EmbeddingStore）里检索和查询内容相关的信息
|
||||
|
return EmbeddingStoreContentRetriever.from(embeddingStore); |
||||
|
} |
||||
|
|
||||
|
@Autowired |
||||
|
private EmbeddingStore embeddingStore; |
||||
|
@Autowired |
||||
|
private EmbeddingModel embeddingModel; |
||||
|
|
||||
|
@Bean |
||||
|
ContentRetriever contentRetrieverXiaozhiPincone() { |
||||
|
// 创建一个 EmbeddingStoreContentRetriever 对象，用于从嵌入存储中检索内容
|
||||
|
return EmbeddingStoreContentRetriever |
||||
|
.builder() |
||||
|
// 设置用于生成嵌入向量的嵌入模型
|
||||
|
.embeddingModel(embeddingModel) |
||||
|
// 指定要使用的嵌入存储
|
||||
|
.embeddingStore(embeddingStore) |
||||
|
// 设置最大检索结果数量，这里表示最多返回 1 条匹配结果
|
||||
|
.maxResults(1) |
||||
|
// 设置最小得分阈值，只有得分大于等于 0.8 的结果才会被返回
|
||||
|
.minScore(0.8) |
||||
|
// 构建最终的 EmbeddingStoreContentRetriever 实例
|
||||
|
.build(); |
||||
|
} |
||||
|
} |
||||
@ -0,0 +1,49 @@ |
|||||
|
package cn.ai.springaigu.controller; |
||||
|
|
||||
|
import cn.ai.springaigu.asistant.XiaozhiAgent; |
||||
|
import cn.ai.springaigu.domain.ChatForm; |
||||
|
import io.swagger.v3.oas.annotations.Operation; |
||||
|
import io.swagger.v3.oas.annotations.tags.Tag; |
||||
|
import org.springframework.beans.factory.annotation.Autowired; |
||||
|
import org.springframework.web.bind.annotation.PostMapping; |
||||
|
import org.springframework.web.bind.annotation.RequestBody; |
||||
|
import org.springframework.web.bind.annotation.RequestMapping; |
||||
|
import org.springframework.web.bind.annotation.RestController; |
||||
|
import reactor.core.publisher.Flux; |
||||
|
|
||||
|
@Tag(name = "硅谷小智") |
||||
|
@RestController |
||||
|
@RequestMapping("/xiaozhi") |
||||
|
public class XiaozhiController { |
||||
|
@Autowired |
||||
|
private XiaozhiAgent xiaozhiAgent; |
||||
|
// @Operation(summary = "对话")
|
||||
|
// @PostMapping("/chat")
|
||||
|
// public String chat(@RequestBody ChatForm chatForm) {
|
||||
|
// return xiaozhiAgent.chat(chatForm.getMemoryId(), chatForm.getMessage());
|
||||
|
// }
|
||||
|
|
||||
|
@Operation(summary = "对话") |
||||
|
@PostMapping(value = "/chat", produces = "text/stream;charset=utf-8") |
||||
|
public Flux<String> chat(@RequestBody ChatForm chatForm) { |
||||
|
return xiaozhiAgent.chat(chatForm.getMemoryId(), chatForm.getMessage()); |
||||
|
} |
||||
|
} |
||||
|
|
||||
|
|
||||
|
// 测试：
|
||||
|
//{
|
||||
|
// "memoryId": 0,
|
||||
|
// "message": "我想预约明天上午的神经内科"
|
||||
|
//}
|
||||
|
// {
|
||||
|
// "memoryId": 1,
|
||||
|
// "message": "帮我预约一下后天的皮肤科，信息是：翠花,523921199204234816,刘医生"
|
||||
|
//}
|
||||
|
//{
|
||||
|
// "memoryId": 1,
|
||||
|
// "message": "对不起，临时有事，帮我取消一下预约"
|
||||
|
//}
|
||||
|
// 我头疼应该怎么处理
|
||||
|
// 姓名：姚翠花、230104190000000000、神经内科、明天下午，最好的医生。
|
||||
|
// 医院的位置在哪，神经内科怎么找
|
||||
@ -0,0 +1,21 @@ |
|||||
|
package cn.ai.springaigu.domain; |
||||
|
|
||||
|
import com.baomidou.mybatisplus.annotation.IdType; |
||||
|
import com.baomidou.mybatisplus.annotation.TableId; |
||||
|
import lombok.AllArgsConstructor; |
||||
|
import lombok.Data; |
||||
|
import lombok.NoArgsConstructor; |
||||
|
|
||||
|
@Data |
||||
|
@AllArgsConstructor |
||||
|
@NoArgsConstructor |
||||
|
public class Appointment { |
||||
|
@TableId(type = IdType.AUTO) |
||||
|
private Long id; |
||||
|
private String username; |
||||
|
private String idCard; |
||||
|
private String department; |
||||
|
private String date; |
||||
|
private String time; |
||||
|
private String doctorName; |
||||
|
} |
||||
@ -0,0 +1,9 @@ |
|||||
|
package cn.ai.springaigu.domain; |
||||
|
|
||||
|
import lombok.Data; |
||||
|
|
||||
|
@Data |
||||
|
public class ChatForm { |
||||
|
private Long memoryId;//对话id
|
||||
|
private String message;//用户问题
|
||||
|
} |
||||
@ -0,0 +1,21 @@ |
|||||
|
package cn.ai.springaigu.domain; |
||||
|
|
||||
|
import lombok.AllArgsConstructor; |
||||
|
import lombok.Data; |
||||
|
import lombok.NoArgsConstructor; |
||||
|
import org.bson.types.ObjectId; |
||||
|
import org.springframework.data.annotation.Id; |
||||
|
import org.springframework.data.mongodb.core.mapping.Document; |
||||
|
|
||||
|
@Data |
||||
|
@AllArgsConstructor |
||||
|
@NoArgsConstructor |
||||
|
@Document("chat_messages") |
||||
|
public class ChatMessages { |
||||
|
|
||||
|
//唯一标识，映射到 MongoDB 文档的 _id 字段
|
||||
|
@Id |
||||
|
private ObjectId id; |
||||
|
private Long messageId; |
||||
|
private String content; //存储当前聊天记录列表的json字符串
|
||||
|
} |
||||
@ -0,0 +1,9 @@ |
|||||
|
package cn.ai.springaigu.mapper; |
||||
|
|
||||
|
import cn.ai.springaigu.domain.Appointment; |
||||
|
import com.baomidou.mybatisplus.core.mapper.BaseMapper; |
||||
|
import org.apache.ibatis.annotations.Mapper; |
||||
|
|
||||
|
@Mapper |
||||
|
public interface AppointmentMapper extends BaseMapper<Appointment> { |
||||
|
} |
||||
@ -0,0 +1,8 @@ |
|||||
|
package cn.ai.springaigu.service; |
||||
|
|
||||
|
import cn.ai.springaigu.domain.Appointment; |
||||
|
import com.baomidou.mybatisplus.extension.service.IService; |
||||
|
|
||||
|
public interface AppointmentService extends IService<Appointment> { |
||||
|
Appointment getOne(Appointment appointment); |
||||
|
} |
||||
@ -0,0 +1,27 @@ |
|||||
|
package cn.ai.springaigu.service.impl; |
||||
|
|
||||
|
import cn.ai.springaigu.domain.Appointment; |
||||
|
import cn.ai.springaigu.mapper.AppointmentMapper; |
||||
|
import cn.ai.springaigu.service.AppointmentService; |
||||
|
import com.baomidou.mybatisplus.core.conditions.query.LambdaQueryWrapper; |
||||
|
import com.baomidou.mybatisplus.extension.service.impl.ServiceImpl; |
||||
|
import org.springframework.stereotype.Service; |
||||
|
|
||||
|
@Service |
||||
|
public class AppointmentServiceImpl extends ServiceImpl<AppointmentMapper, Appointment> implements AppointmentService { |
||||
|
/** |
||||
|
* 查询订单是否存在 |
||||
|
* |
||||
|
* @param appointment |
||||
|
*/ |
||||
|
@Override |
||||
|
public Appointment getOne(Appointment appointment) { |
||||
|
LambdaQueryWrapper<Appointment> queryWrapper = new LambdaQueryWrapper<>(); |
||||
|
queryWrapper.eq(Appointment::getUsername, appointment.getUsername()); |
||||
|
queryWrapper.eq(Appointment::getIdCard, appointment.getIdCard()); |
||||
|
queryWrapper.eq(Appointment::getDepartment, appointment.getDepartment()); |
||||
|
queryWrapper.eq(Appointment::getDate, appointment.getDate()); |
||||
|
queryWrapper.eq(Appointment::getTime, appointment.getTime()); |
||||
|
return baseMapper.selectOne(queryWrapper); |
||||
|
} |
||||
|
} |
||||
@ -0,0 +1,46 @@ |
|||||
|
package cn.ai.springaigu.store; |
||||
|
|
||||
|
import cn.ai.springaigu.domain.ChatMessages; |
||||
|
import dev.langchain4j.data.message.ChatMessage; |
||||
|
import dev.langchain4j.data.message.ChatMessageDeserializer; |
||||
|
import dev.langchain4j.data.message.ChatMessageSerializer; |
||||
|
import dev.langchain4j.store.memory.chat.ChatMemoryStore; |
||||
|
import org.springframework.beans.factory.annotation.Autowired; |
||||
|
import org.springframework.data.mongodb.core.MongoTemplate; |
||||
|
import org.springframework.data.mongodb.core.query.Criteria; |
||||
|
import org.springframework.data.mongodb.core.query.Query; |
||||
|
import org.springframework.data.mongodb.core.query.Update; |
||||
|
import org.springframework.stereotype.Component; |
||||
|
|
||||
|
import java.util.LinkedList; |
||||
|
import java.util.List; |
||||
|
|
||||
|
@Component |
||||
|
public class MongoChatMemoryStore implements ChatMemoryStore { |
||||
|
@Autowired |
||||
|
private MongoTemplate mongoTemplate; |
||||
|
@Override |
||||
|
public List<ChatMessage> getMessages(Object memoryId) { |
||||
|
Criteria criteria = Criteria.where("memoryId").is(memoryId); |
||||
|
Query query = new Query(criteria); |
||||
|
ChatMessages chatMessages = mongoTemplate.findOne(query, ChatMessages.class); |
||||
|
if(chatMessages == null) return new LinkedList<>(); |
||||
|
return ChatMessageDeserializer.messagesFromJson(chatMessages.getContent()); |
||||
|
} |
||||
|
|
||||
|
@Override |
||||
|
public void updateMessages(Object memoryId, List<ChatMessage> messages) { |
||||
|
Criteria criteria = Criteria.where("memoryId").is(memoryId); |
||||
|
Query query = new Query(criteria); |
||||
|
Update update = new Update(); |
||||
|
update.set("content", ChatMessageSerializer.messagesToJson(messages)); |
||||
|
//根据query条件能查询出文档，则修改文档；否则新增文档
|
||||
|
mongoTemplate.upsert(query, update, ChatMessages.class); |
||||
|
} |
||||
|
@Override |
||||
|
public void deleteMessages(Object memoryId) { |
||||
|
Criteria criteria = Criteria.where("memoryId").is(memoryId); |
||||
|
Query query = new Query(criteria); |
||||
|
mongoTemplate.remove(query, ChatMessages.class); |
||||
|
} |
||||
|
} |
||||
@ -0,0 +1,66 @@ |
|||||
|
package cn.ai.springaigu.tools; |
||||
|
|
||||
|
import cn.ai.springaigu.domain.Appointment; |
||||
|
import cn.ai.springaigu.service.AppointmentService; |
||||
|
import dev.langchain4j.agent.tool.P; |
||||
|
import dev.langchain4j.agent.tool.Tool; |
||||
|
import org.springframework.beans.factory.annotation.Autowired; |
||||
|
import org.springframework.stereotype.Component; |
||||
|
|
||||
|
@Component |
||||
|
public class AppointmentTools { |
||||
|
|
||||
|
@Autowired |
||||
|
private AppointmentService appointmentService; |
||||
|
|
||||
|
// @Tool(name = "预约挂号", value = "根据参数，先执行工具方法queryDepartment查询是否可预约，并直接给用户回答是否可预约，并让用户确认所有预约信息，用户确认后再进行预约。")
|
||||
|
@Tool(name="预约挂号", value = "根据参数，先执行工具方法queryDepartment查询是否可预约，并直接给用户回答是否可预约，并让用户确认所有预约信息，用户确认后再进行预约。如果用户没有提供具体的医生姓名，请从向量存储中找到一位医生。") |
||||
|
public String bookAppointment(Appointment appointment) { |
||||
|
//查找数据库中是否包含对应的预约记录
|
||||
|
Appointment appointmentDB = appointmentService.getOne(appointment); |
||||
|
if (appointmentDB == null) { |
||||
|
appointment.setId(null);//防止大模型幻觉设置了id
|
||||
|
if (appointmentService.save(appointment)) { |
||||
|
return "预约成功，并返回预约详情"; |
||||
|
} else { |
||||
|
return "预约失败"; |
||||
|
} |
||||
|
} |
||||
|
return "您在相同的科室和时间已有预约"; |
||||
|
} |
||||
|
|
||||
|
|
||||
|
@Tool(name = "取消预约挂号", value = "根据参数，查询预约是否存在，如果存在则删除预约记录并返回取消预约成功，否则返回取消预约失败") |
||||
|
public String cancelAppointment(Appointment appointment) { |
||||
|
Appointment appointmentDB = appointmentService.getOne(appointment); |
||||
|
if (appointmentDB != null) { |
||||
|
//删除预约记录
|
||||
|
if (appointmentService.removeById(appointmentDB.getId())) { |
||||
|
return "取消预约成功"; |
||||
|
} else { |
||||
|
return "取消预约失败"; |
||||
|
} |
||||
|
} |
||||
|
//取消失败
|
||||
|
return "您没有预约记录，请核对预约科室和时间"; |
||||
|
} |
||||
|
|
||||
|
@Tool(name = "查询是否有号源", value = "根据科室名称，日期，时间和医生查询是否有号源，并返回给用户") |
||||
|
public boolean queryDepartment( |
||||
|
@P(value = "科室名称") String name, |
||||
|
@P(value = "日期") String date, |
||||
|
@P(value = "时间，可选值：上午、下午") String time, |
||||
|
@P(value = "医生名称", required = false) String doctorName |
||||
|
) { |
||||
|
System.out.println("查询是否有号源"); |
||||
|
System.out.println("科室名称：" + name); |
||||
|
System.out.println("日期：" + date); |
||||
|
System.out.println("时间：" + time); |
||||
|
System.out.println("医生名称：" + doctorName); |
||||
|
//TODO 维护医生的排班信息：
|
||||
|
//如果没有指定医生名字，则根据其他条件查询是否有可以预约的医生（有返回true，否则返回false）；
|
||||
|
//如果指定了医生名字，则判断医生是否有排班（没有排版返回false）
|
||||
|
//如果有排班，则判断医生排班时间段是否已约满（约满返回false，有空闲时间返回true）
|
||||
|
return true; |
||||
|
} |
||||
|
} |
||||
@ -0,0 +1,36 @@ |
|||||
|
package cn.ai.springaigu.tools; |
||||
|
|
||||
|
import dev.langchain4j.agent.tool.P; |
||||
|
import dev.langchain4j.agent.tool.Tool; |
||||
|
import dev.langchain4j.agent.tool.ToolMemoryId; |
||||
|
import org.springframework.stereotype.Component; |
||||
|
|
||||
|
@Component |
||||
|
public class CalculatorTools { |
||||
|
|
||||
|
// @Tool
|
||||
|
// double sum(double a, double b) {
|
||||
|
// System.out.println("调用加法运算");
|
||||
|
// return a + b;
|
||||
|
// }
|
||||
|
// @Tool
|
||||
|
// double squareRoot(double x) {
|
||||
|
// System.out.println("调用平方根运算");
|
||||
|
// return Math.sqrt(x);
|
||||
|
// }
|
||||
|
|
||||
|
@Tool(name = "加法", value = "返回两个参数相加之和") |
||||
|
double sum( |
||||
|
@ToolMemoryId int memoryId, |
||||
|
@P(value="加数1", required = true) double a, |
||||
|
@P(value="加数2", required = true) double b) { |
||||
|
System.out.println("调用加法运算 " + memoryId); |
||||
|
return a + b; |
||||
|
} |
||||
|
@Tool(name = "平方根", value = "返回给定参数的平方根") |
||||
|
double squareRoot( |
||||
|
@ToolMemoryId int memoryId, double x) { |
||||
|
System.out.println("调用平方根运算 " + memoryId); |
||||
|
return Math.sqrt(x); |
||||
|
} |
||||
|
} |
||||
@ -0,0 +1,46 @@ |
|||||
|
# langchain4j测试模型 |
||||
|
langchain4j: |
||||
|
open-ai: |
||||
|
chat-model: |
||||
|
base-url: https://api.deepseek.com |
||||
|
api-key: sk-d2c693bbb4c64956809a03d7f40e3e70 |
||||
|
# 推理模型 deepseek-reasoner |
||||
|
model-name: deepseek-chat |
||||
|
log-requests: true |
||||
|
log-responses: true |
||||
|
community: |
||||
|
dashscope: |
||||
|
chat-model: |
||||
|
api-key: sk-10a57fb7b31e4806ab5c4ea6ffa02f5a |
||||
|
model-name: qwen-max |
||||
|
embedding-model: |
||||
|
api-key: sk-10a57fb7b31e4806ab5c4ea6ffa02f5a |
||||
|
model-name: text-embedding-v3 |
||||
|
streaming-chat-model: |
||||
|
api-key: sk-10a57fb7b31e4806ab5c4ea6ffa02f5a |
||||
|
model-name: qwen-plus |
||||
|
|
||||
|
spring: |
||||
|
data: |
||||
|
mongodb: |
||||
|
uri: mongodb://localhost:27017/chat_memory_db |
||||
|
datasource: |
||||
|
driver-class-name: com.mysql.cj.jdbc.Driver |
||||
|
url: jdbc:mysql://117.72.43.105:3306/ai_study?useUnicode=true&characterEncoding=UTF-8&serverTimezone=Asia/Shanghai&useSSL=false |
||||
|
username: root |
||||
|
password: Yuan625621105. |
||||
|
|
||||
|
pinecone: |
||||
|
api-key: pcsk_5qjBiY_FrVVSC7NHrMem43SrCJ4CK6EGXduToZKZmJ4dGFDonGJPmVux2mQNiyqypTvkVx |
||||
|
|
||||
|
# 开启 SQL 日志打印 |
||||
|
mybatis-plus: |
||||
|
configuration: |
||||
|
log-impl: org.apache.ibatis.logging.stdout.StdOutImpl |
||||
|
|
||||
|
|
||||
|
|
||||
|
# 启用日志debug级别 |
||||
|
logging: |
||||
|
level: |
||||
|
root: info |
||||
@ -0,0 +1,5 @@ |
|||||
|
<?xml version="1.0" encoding="UTF-8"?> |
||||
|
<!DOCTYPE mapper PUBLIC "-//mybatis.org//DTD Mapper 3.0//EN" |
||||
|
"http://mybatis.org/dtd/mybatis-3-mapper.dtd"> |
||||
|
<mapper namespace="cn.ai.springaigu.mapper.AppointmentMapper"> |
||||
|
</mapper> |
||||
@ -0,0 +1,3 @@ |
|||||
|
你是我的好朋友，请用东北话回答问题，回答问题的时候适当添加表情符号。 |
||||
|
今天是 {{current_date}}。 |
||||
|
|
||||
@ -0,0 +1,3 @@ |
|||||
|
你是我的好朋友，我是{{username}}，我的年龄是{{age}}，请用东北话回答问题，回答问题的时候适当添加表情 |
||||
|
符号。 |
||||
|
今天是 {{current_date}}。 |
||||
@ -0,0 +1,18 @@ |
|||||
|
你的名字是“硅谷小智”，你是一家名为“北京协和医院”的智能客服。 |
||||
|
你是一个训练有素的医疗顾问和医疗伴诊助手。 |
||||
|
你态度友好、礼貌且言辞简洁。 |
||||
|
1、请仅在用户发起第一次会话时，和用户打个招呼，并介绍你是谁。 |
||||
|
2、作为一个训练有素的医疗顾问： |
||||
|
请基于当前临床实践和研究，针对患者提出的特定健康问题，提供详细、准确且实用的医疗建议。请同时考虑可能的病 |
||||
|
因、诊断流程、治疗方案以及预防措施，并给出在不同情境下的应对策略。对于药物治疗，请特别指明适用的药品名 |
||||
|
称、剂量和疗程。如果需要进一步的检查或就医，也请明确指示。 |
||||
|
3、作为医疗伴诊助手，你可以回答用户就医流程中的相关问题，主要包含以下功能： |
||||
|
AI分导诊：根据患者的病情和就医需求，智能推荐最合适的科室。 |
||||
|
AI挂号助手：实现智能查询是否有挂号号源服务；实现智能预约挂号服务；实现智能取消挂号服务。 |
||||
|
4、你必须遵守的规则如下： |
||||
|
在获取挂号预约详情或取消挂号预约之前，你必须确保自己知晓用户的姓名（必选）、身份证号（必选）、预约科室 |
||||
|
（必选）、预约日期（必选，格式举例：2025-04-14）、预约时间（必选，格式：上午 或 下午）、预约医生（可 |
||||
|
选）。 |
||||
|
当被问到其他领域的咨询时，要表示歉意并说明你无法在这方面提供帮助。 |
||||
|
5、请在回答的结果中适当包含一些轻松可爱的图标和表情。 |
||||
|
6、今天是 {{current_date}}。 |
||||
@ -0,0 +1,42 @@ |
|||||
|
package cn.ai.springaigu; |
||||
|
|
||||
|
import cn.ai.springaigu.domain.Appointment; |
||||
|
import cn.ai.springaigu.service.AppointmentService; |
||||
|
import org.junit.jupiter.api.Test; |
||||
|
import org.springframework.beans.factory.annotation.Autowired; |
||||
|
import org.springframework.boot.test.context.SpringBootTest; |
||||
|
|
||||
|
@SpringBootTest |
||||
|
class AppointmentServiceTest { |
||||
|
@Autowired |
||||
|
private AppointmentService appointmentService; |
||||
|
|
||||
|
@Test |
||||
|
void testGetOne() { |
||||
|
Appointment appointment = new Appointment(); |
||||
|
appointment.setUsername("张三"); |
||||
|
appointment.setIdCard("123456789012345678"); |
||||
|
appointment.setDepartment("内科"); |
||||
|
appointment.setDate("2025-04-14"); |
||||
|
appointment.setTime("上午"); |
||||
|
Appointment appointmentDB = appointmentService.getOne(appointment); |
||||
|
System.out.println(appointmentDB); |
||||
|
} |
||||
|
|
||||
|
@Test |
||||
|
void testSave() { |
||||
|
Appointment appointment = new Appointment(); |
||||
|
appointment.setUsername("张三"); |
||||
|
appointment.setIdCard("123456789012345678"); |
||||
|
appointment.setDepartment("内科"); |
||||
|
appointment.setDate("2025-04-14"); |
||||
|
appointment.setTime("上午"); |
||||
|
appointment.setDoctorName("张医生"); |
||||
|
appointmentService.save(appointment); |
||||
|
} |
||||
|
|
||||
|
@Test |
||||
|
void testRemoveById() { |
||||
|
appointmentService.removeById(1L); |
||||
|
} |
||||
|
} |
||||
@ -0,0 +1,94 @@ |
|||||
|
package cn.ai.springaigu; |
||||
|
|
||||
|
import dev.langchain4j.data.document.Document; |
||||
|
import dev.langchain4j.data.document.loader.FileSystemDocumentLoader; |
||||
|
import dev.langchain4j.data.embedding.Embedding; |
||||
|
import dev.langchain4j.data.segment.TextSegment; |
||||
|
import dev.langchain4j.model.embedding.EmbeddingModel; |
||||
|
import dev.langchain4j.model.output.Response; |
||||
|
import dev.langchain4j.store.embedding.*; |
||||
|
import org.junit.jupiter.api.Test; |
||||
|
import org.springframework.beans.factory.annotation.Autowired; |
||||
|
import org.springframework.boot.test.context.SpringBootTest; |
||||
|
|
||||
|
import java.util.Arrays; |
||||
|
import java.util.List; |
||||
|
|
||||
|
@SpringBootTest |
||||
|
public class EmbeddingTest { |
||||
|
|
||||
|
@Autowired |
||||
|
private EmbeddingModel embeddingModel; |
||||
|
|
||||
|
@Test |
||||
|
public void testEmbeddingModel() { |
||||
|
Response<Embedding> embed = embeddingModel.embed("你好"); |
||||
|
System.out.println("向量维度：" + embed.content().vector().length); |
||||
|
System.out.println("向量输出：" + embed.toString()); |
||||
|
} |
||||
|
|
||||
|
// 测试向量存储
|
||||
|
@Autowired |
||||
|
private EmbeddingStore embeddingStore; |
||||
|
|
||||
|
|
||||
|
/** |
||||
|
* 将文本转换成向量，然后存储到pinecone中 |
||||
|
* <p> |
||||
|
* 参考： |
||||
|
* https://docs.langchain4j.dev/tutorials/embedding-stores
|
||||
|
*/ |
||||
|
@Test |
||||
|
public void testPineconeEmbeded() { |
||||
|
//将文本转换成向量
|
||||
|
TextSegment segment1 = TextSegment.from("我喜欢羽毛球"); |
||||
|
Embedding embedding1 = embeddingModel.embed(segment1).content(); |
||||
|
//存入向量数据库
|
||||
|
embeddingStore.add(embedding1, segment1); |
||||
|
TextSegment segment2 = TextSegment.from("今天天气很好"); |
||||
|
Embedding embedding2 = embeddingModel.embed(segment2).content(); |
||||
|
embeddingStore.add(embedding2, segment2); |
||||
|
} |
||||
|
|
||||
|
/** |
||||
|
* Pinecone-相似度匹配 |
||||
|
*/ |
||||
|
@Test |
||||
|
public void embeddingSearch() { |
||||
|
//提问，并将问题转成向量数据
|
||||
|
Embedding queryEmbedding = embeddingModel.embed("你最喜欢的运动是什么？").content(); |
||||
|
//创建搜索请求对象
|
||||
|
EmbeddingSearchRequest searchRequest = EmbeddingSearchRequest.builder() |
||||
|
.queryEmbedding(queryEmbedding) |
||||
|
.maxResults(1) //匹配最相似的一条记录
|
||||
|
//.minScore(0.8)
|
||||
|
.build(); |
||||
|
//根据搜索请求 searchRequest 在向量存储中进行相似度搜索
|
||||
|
EmbeddingSearchResult<TextSegment> searchResult = embeddingStore.search(searchRequest); |
||||
|
//searchResult.matches()：获取搜索结果中的匹配项列表。
|
||||
|
//.get(0)：从匹配项列表中获取第一个匹配项
|
||||
|
EmbeddingMatch<TextSegment> embeddingMatch = searchResult.matches().get(0); |
||||
|
//获取匹配项的相似度得分
|
||||
|
System.out.println(embeddingMatch.score()); // 0.8144288515898701
|
||||
|
//返回文本结果
|
||||
|
System.out.println(embeddingMatch.embedded().text()); |
||||
|
} |
||||
|
|
||||
|
// 上传知识库到Pinecone
|
||||
|
@Test |
||||
|
public void testUploadKnowledgeLibrary() { |
||||
|
//使用FileSystemDocumentLoader读取指定目录下的知识库文档
|
||||
|
//并使用默认的文档解析器对文档进行解析
|
||||
|
Document document1 = FileSystemDocumentLoader.loadDocument("D:/学习/课件/knowledge/医院信息.md"); |
||||
|
Document document2 = FileSystemDocumentLoader.loadDocument("D:/学习/课件/knowledge/科室信息.md"); |
||||
|
Document document3 = FileSystemDocumentLoader.loadDocument("D:/学习/课件/knowledge/神经内科.md"); |
||||
|
List<Document> documents = Arrays.asList(document1, document2, document3); |
||||
|
//文本向量化并存入向量数据库：将每个片段进行向量化，得到一个嵌入向量
|
||||
|
EmbeddingStoreIngestor |
||||
|
.builder() |
||||
|
.embeddingStore(embeddingStore) |
||||
|
.embeddingModel(embeddingModel) |
||||
|
.build() |
||||
|
.ingest(documents); |
||||
|
} |
||||
|
} |
||||
@ -0,0 +1,77 @@ |
|||||
|
package cn.ai.springaigu; |
||||
|
|
||||
|
import cn.ai.springaigu.domain.ChatMessages; |
||||
|
import org.junit.jupiter.api.Test; |
||||
|
import org.springframework.beans.factory.annotation.Autowired; |
||||
|
import org.springframework.boot.test.context.SpringBootTest; |
||||
|
import org.springframework.data.mongodb.core.MongoTemplate; |
||||
|
import org.springframework.data.mongodb.core.query.Criteria; |
||||
|
import org.springframework.data.mongodb.core.query.Query; |
||||
|
import org.springframework.data.mongodb.core.query.Update; |
||||
|
|
||||
|
@SpringBootTest |
||||
|
public class MongoCrudTest { |
||||
|
@Autowired |
||||
|
private MongoTemplate mongoTemplate; |
||||
|
|
||||
|
/** |
||||
|
* 插入文档 |
||||
|
*/ |
||||
|
/* |
||||
|
@Test |
||||
|
public void testInsert() { |
||||
|
mongoTemplate.insert(new ChatMessages(1L, "聊天记录")); |
||||
|
}*/ |
||||
|
|
||||
|
/** |
||||
|
* 插入文档 |
||||
|
*/ |
||||
|
@Test |
||||
|
public void testInsert2() { |
||||
|
ChatMessages chatMessages = new ChatMessages(); |
||||
|
chatMessages.setContent("聊天记录列表"); |
||||
|
mongoTemplate.insert(chatMessages); |
||||
|
} |
||||
|
|
||||
|
/** |
||||
|
* 根据id查询文档 |
||||
|
*/ |
||||
|
@Test |
||||
|
public void testFindById() { |
||||
|
ChatMessages chatMessages = mongoTemplate.findById("6801ead733ba9c4a0d9b6c7b", ChatMessages.class); |
||||
|
System.out.println(chatMessages); |
||||
|
} |
||||
|
/** |
||||
|
* 修改文档 |
||||
|
*/ |
||||
|
@Test |
||||
|
public void testUpdate() { |
||||
|
Criteria criteria = Criteria.where("_id").is("6801ead733ba9c4a0d9b6c7b"); |
||||
|
Query query = new Query(criteria); |
||||
|
Update update = new Update(); |
||||
|
update.set("content", "新的聊天记录列表"); |
||||
|
//修改或新增
|
||||
|
mongoTemplate.upsert(query, update, ChatMessages.class); |
||||
|
} |
||||
|
/** |
||||
|
* 新增或修改文档 |
||||
|
*/ |
||||
|
@Test |
||||
|
public void testUpdate2() { |
||||
|
Criteria criteria = Criteria.where("_id").is("100"); |
||||
|
Query query = new Query(criteria); |
||||
|
Update update = new Update(); |
||||
|
update.set("content", "新的聊天记录列表"); |
||||
|
//修改或新增
|
||||
|
mongoTemplate.upsert(query, update, ChatMessages.class); |
||||
|
} |
||||
|
/** |
||||
|
* 删除文档 |
||||
|
*/ |
||||
|
@Test |
||||
|
public void testDelete() { |
||||
|
Criteria criteria = Criteria.where("_id").is("100"); |
||||
|
Query query = new Query(criteria); |
||||
|
mongoTemplate.remove(query, ChatMessages.class); |
||||
|
} |
||||
|
} |
||||
@ -0,0 +1,137 @@ |
|||||
|
package cn.ai.springaigu; |
||||
|
|
||||
|
import dev.langchain4j.data.document.Document; |
||||
|
import dev.langchain4j.data.document.loader.FileSystemDocumentLoader; |
||||
|
import dev.langchain4j.data.document.parser.TextDocumentParser; |
||||
|
import dev.langchain4j.data.document.parser.apache.pdfbox.ApachePdfBoxDocumentParser; |
||||
|
import dev.langchain4j.data.document.splitter.DocumentByParagraphSplitter; |
||||
|
import dev.langchain4j.data.embedding.Embedding; |
||||
|
import dev.langchain4j.data.message.UserMessage; |
||||
|
import dev.langchain4j.data.segment.TextSegment; |
||||
|
import dev.langchain4j.model.embedding.EmbeddingModel; |
||||
|
import dev.langchain4j.model.embedding.onnx.HuggingFaceTokenizer; |
||||
|
import dev.langchain4j.store.embedding.*; |
||||
|
import dev.langchain4j.store.embedding.inmemory.InMemoryEmbeddingStore; |
||||
|
import org.junit.jupiter.api.Test; |
||||
|
import org.springframework.beans.factory.annotation.Autowired; |
||||
|
import org.springframework.boot.test.context.SpringBootTest; |
||||
|
|
||||
|
import java.nio.file.FileSystems; |
||||
|
import java.nio.file.PathMatcher; |
||||
|
import java.util.Arrays; |
||||
|
import java.util.List; |
||||
|
|
||||
|
|
||||
|
@SpringBootTest |
||||
|
public class RAGTest { |
||||
|
|
||||
|
|
||||
|
// 文档加载
|
||||
|
@Test |
||||
|
public void testReadDocument() { |
||||
|
// // 加载单个文档
|
||||
|
// Document document = FileSystemDocumentLoader.loadDocument("E:/knowledge/file.txt", new TextDocumentParser());
|
||||
|
// // 从一个目录中加载所有文档
|
||||
|
// List<Document> documents = FileSystemDocumentLoader.loadDocuments("E:/knowledge", new TextDocumentParser());
|
||||
|
// // 从一个目录中加载所有的.txt文档
|
||||
|
// PathMatcher pathMatcher = FileSystems.getDefault().getPathMatcher("glob:*.txt");
|
||||
|
// List<Document> documents = FileSystemDocumentLoader.loadDocuments("E:/knowledge",pathMatcher, new TextDocumentParser());
|
||||
|
// // 从一个目录及其子目录中加载所有文档
|
||||
|
// List<Document> documents = FileSystemDocumentLoader.loadDocumentsRecursively("E:/knowledge", new TextDocumentParser());
|
||||
|
|
||||
|
//使用FileSystemDocumentLoader读取指定目录下的知识库文档
|
||||
|
//并使用默认的文档解析器TextDocumentParser对文档进行解析
|
||||
|
Document document = FileSystemDocumentLoader.loadDocument("D:\\IdeaProjects\\learn\\Spring-Ai-Gu\\src\\main\\resources\\my-prompt-template.txt"); |
||||
|
System.out.println(document.text()); |
||||
|
} |
||||
|
|
||||
|
// 来自 langchain4j 模块的文本文档解析器（TextDocumentParser），它能够解析纯文本格式的文件（例如 TXT、HTML、MD 等）。
|
||||
|
// ApachePdfBoxDocumentParser 解析PDF
|
||||
|
// ApachePoiDocumentParser 解析微软办公软件的文件格式（例如 DOC、DOCX、PPT、PPTX、XLS、XLSX 等）
|
||||
|
// ApacheTikaDocumentParser 以自动检测并解析几乎所有现有的文件格式
|
||||
|
|
||||
|
/** |
||||
|
* 解析PDF |
||||
|
*/ |
||||
|
@Test |
||||
|
public void testParsePDF() { |
||||
|
Document document = FileSystemDocumentLoader.loadDocument("C:\\Users\\admin\\Desktop\\工作\\幼儿园E-portfolio\\Biannual Portfolios\\N1：认知、语言.pdf", new ApachePdfBoxDocumentParser()); |
||||
|
System.out.println(document); |
||||
|
} |
||||
|
|
||||
|
// 文档分割
|
||||
|
// 按段落文档分割器（DocumentByParagraphSplitter）
|
||||
|
// 按行文档分割器（DocumentByLineSplitter）
|
||||
|
// 按句子文档分割器（DocumentBySentenceSplitter）
|
||||
|
// 按单词文档分割器（DocumentByWordSplitter）
|
||||
|
// 按字符文档分割器（DocumentByCharacterSplitter）
|
||||
|
// 按正则表达式文档分割器（DocumentByRegexSplitter）
|
||||
|
// 递归分割：DocumentSplitters.recursive (...)
|
||||
|
// 默认情况下每个文本片段最多不能超过300个token
|
||||
|
|
||||
|
/** |
||||
|
* 加载文档并存入向量数据库 |
||||
|
*/ |
||||
|
@Test |
||||
|
public void testReadDocumentAndStore() { |
||||
|
//使用FileSystemDocumentLoader读取指定目录下的知识库文档
|
||||
|
//并使用默认的文档解析器对文档进行解析(TextDocumentParser)
|
||||
|
Document document = FileSystemDocumentLoader.loadDocument("C:\\Users\\admin\\Desktop\\评估报告.md"); |
||||
|
//为了简单起见，我们暂时使用基于内存的向量存储
|
||||
|
InMemoryEmbeddingStore<TextSegment> embeddingStore = new InMemoryEmbeddingStore<>(); |
||||
|
//ingest
|
||||
|
//1、分割文档：默认使用递归分割器，将文档分割为多个文本片段，每个片段包含不超过 300个token，并且 有 30 个token的重叠部分保证连贯性
|
||||
|
//DocumentByParagraphSplitter(DocumentByLineSplitter(DocumentBySentenceSplitter(DocumentByWordSplitter)))
|
||||
|
//2、文本向量化：使用一个LangChain4j内置的轻量化向量模型对每个文本片段进行向量化
|
||||
|
//3、将原始文本和向量存储到向量数据库中(InMemoryEmbeddingStore)
|
||||
|
EmbeddingStoreIngestor.ingest(document, embeddingStore); |
||||
|
//查看向量数据库内容
|
||||
|
System.out.println(embeddingStore); |
||||
|
} |
||||
|
|
||||
|
|
||||
|
/** |
||||
|
* 文档分割 |
||||
|
*/ |
||||
|
@Test |
||||
|
public void testDocumentSplitter() { |
||||
|
//使用FileSystemDocumentLoader读取指定目录下的知识库文档
|
||||
|
//并使用默认的文档解析器对文档进行解析(TextDocumentParser)
|
||||
|
Document document = FileSystemDocumentLoader.loadDocument("C:\\Users\\admin\\Desktop\\评估报告.md"); |
||||
|
//为了简单起见，我们暂时使用基于内存的向量存储
|
||||
|
InMemoryEmbeddingStore<TextSegment> embeddingStore = new InMemoryEmbeddingStore<>(); |
||||
|
//自定义文档分割器
|
||||
|
//按段落分割文档：每个片段包含不超过 300个token，并且有 30个token的重叠部分保证连贯性
|
||||
|
//注意：当段落长度总和小于设定的最大长度时，就不会有重叠的必要。
|
||||
|
DocumentByParagraphSplitter documentSplitter = new DocumentByParagraphSplitter( |
||||
|
300, |
||||
|
30, |
||||
|
//token分词器：按token计算
|
||||
|
new HuggingFaceTokenizer()); |
||||
|
//按字符计算
|
||||
|
//DocumentByParagraphSplitter documentSplitter = new DocumentByParagraphSplitter(300, 30);
|
||||
|
EmbeddingStoreIngestor |
||||
|
.builder() |
||||
|
.embeddingStore(embeddingStore) |
||||
|
.documentSplitter(documentSplitter) |
||||
|
.build() |
||||
|
.ingest(document); |
||||
|
} |
||||
|
|
||||
|
/** |
||||
|
* 计算 token 长度 |
||||
|
*/ |
||||
|
@Test |
||||
|
public void testTokenCount() { |
||||
|
String text = "这是一个示例文本，用于测试 token 长度的计算。"; |
||||
|
UserMessage userMessage = UserMessage.userMessage(text); |
||||
|
//计算 token 长度
|
||||
|
//QwenTokenizer tokenizer = new QwenTokenizer(System.getenv("DASH_SCOPE_API_KEY"),"qwen-max");
|
||||
|
HuggingFaceTokenizer tokenizer = new HuggingFaceTokenizer(); |
||||
|
int count = tokenizer.estimateTokenCountInMessage(userMessage); |
||||
|
System.out.println("token长度：" + count); |
||||
|
} |
||||
|
|
||||
|
|
||||
|
|
||||
|
} |
||||
@ -0,0 +1,196 @@ |
|||||
|
package cn.ai.springaigu; |
||||
|
|
||||
|
import cn.ai.springaigu.asistant.Assistant; |
||||
|
import cn.ai.springaigu.asistant.MemoryChatAssistant; |
||||
|
import cn.ai.springaigu.asistant.SeparateChatAssistant; |
||||
|
import dev.langchain4j.community.model.dashscope.QwenChatModel; |
||||
|
//import dev.langchain4j.model.openai.OpenAiChatModel;
|
||||
|
import dev.langchain4j.community.model.dashscope.WanxImageModel; |
||||
|
import dev.langchain4j.data.image.Image; |
||||
|
import dev.langchain4j.data.message.AiMessage; |
||||
|
import dev.langchain4j.data.message.UserMessage; |
||||
|
import dev.langchain4j.memory.chat.MessageWindowChatMemory; |
||||
|
import dev.langchain4j.model.chat.response.ChatResponse; |
||||
|
import dev.langchain4j.model.output.Response; |
||||
|
import dev.langchain4j.service.AiServices; |
||||
|
import org.junit.jupiter.api.Test; |
||||
|
import org.springframework.beans.factory.annotation.Autowired; |
||||
|
import org.springframework.beans.factory.annotation.Value; |
||||
|
import org.springframework.boot.test.context.SpringBootTest; |
||||
|
|
||||
|
import java.util.Arrays; |
||||
|
|
||||
|
|
||||
|
@SpringBootTest |
||||
|
public class TestGPT { |
||||
|
|
||||
|
// /**
|
||||
|
// * 整合SpringBoot
|
||||
|
// */
|
||||
|
// @Autowired
|
||||
|
// private OpenAiChatModel openAiChatModel;
|
||||
|
@Value("${langchain4j.community.dashscope.chat-model.api-key}") |
||||
|
private String apiKey; |
||||
|
/** |
||||
|
* 通义千问大模型 |
||||
|
*/ |
||||
|
@Autowired |
||||
|
private QwenChatModel qwenChatModel; |
||||
|
|
||||
|
@Autowired |
||||
|
private Assistant assistant; |
||||
|
|
||||
|
/** |
||||
|
* gpt-4o-mini语言模型接入测试 |
||||
|
* 如果你暂时没有密钥，也可以使用LangChain4j 提供的演示密钥，这个密钥是免费的，有使用配额限制，且仅限于 gpt-4o-mini 模型。 |
||||
|
*/ |
||||
|
// @Test
|
||||
|
// public void testGPTDemo() {
|
||||
|
// //初始化模型
|
||||
|
// OpenAiChatModel model = OpenAiChatModel.builder()
|
||||
|
// //LangChain4j提供的代理服务器，该代理服务器会将演示密钥替换成真实密钥， 再将请求转发给OpenAI API
|
||||
|
// .baseUrl("http://langchain4j.dev/demo/openai/v1") //设置模型api地址（如果apiKey = "demo"，则可省略baseUrl的配置）
|
||||
|
// .apiKey("demo") //设置模型apiKey
|
||||
|
// .modelName("gpt-4o-mini") //设置模型名称
|
||||
|
// .build();
|
||||
|
// //向模型提问
|
||||
|
// String answer = model.chat("你好");
|
||||
|
// //输出结果
|
||||
|
// System.out.println(answer);
|
||||
|
// }
|
||||
|
//
|
||||
|
// @Test
|
||||
|
// public void testSpringBoot() {
|
||||
|
// //向模型提问
|
||||
|
// String answer = openAiChatModel.chat("你好");
|
||||
|
// //输出结果
|
||||
|
// System.out.println(answer);
|
||||
|
// }
|
||||
|
@Test |
||||
|
public void testDashScopeQwen() { |
||||
|
//向模型提问
|
||||
|
String answer = qwenChatModel.chat("你好"); |
||||
|
//输出结果
|
||||
|
System.out.println(answer); |
||||
|
} |
||||
|
|
||||
|
@Test |
||||
|
public void testDashScopeWanx() { |
||||
|
WanxImageModel wanxImageModel = WanxImageModel.builder() |
||||
|
.modelName("wanx2.1-t2i-plus") |
||||
|
.apiKey(apiKey) |
||||
|
.build(); |
||||
|
Response<Image> response = wanxImageModel.generate("奇幻森林精灵：在一片弥漫着轻柔薄雾的 古老森林深处，阳光透过茂密枝叶洒下金色光斑。一位身材娇小、长着透明薄翼的精灵少女站在一朵硕大的蘑菇上。她 有着海藻般的绿色长发，发间点缀着蓝色的小花，皮肤泛着珍珠般的微光。身上穿着由翠绿树叶和白色藤蔓编织而成的 连衣裙，手中捧着一颗散发着柔和光芒的水晶球，周围环绕着五彩斑斓的蝴蝶，脚下是铺满苔藓的地面，蘑菇和蕨类植物丛生，营造出神秘而梦幻的氛围。"); |
||||
|
System.out.println(response.content().url()); |
||||
|
} |
||||
|
|
||||
|
@Test |
||||
|
public void testChat() { |
||||
|
//创建AIService
|
||||
|
Assistant assistant = AiServices.create(Assistant.class, qwenChatModel); |
||||
|
//调用service的接口
|
||||
|
String answer = assistant.chat("Hello"); |
||||
|
System.out.println(answer); |
||||
|
} |
||||
|
//也可以在 Assistant 接口上添加 @AiService 注解
|
||||
|
@Test |
||||
|
public void testAssistant() { |
||||
|
String answer = assistant.chat("Hello"); |
||||
|
System.out.println(answer); |
||||
|
} |
||||
|
|
||||
|
// 测试对话是否有记忆
|
||||
|
@Test |
||||
|
public void testChatMemory() { |
||||
|
String answer1 = assistant.chat("我是环环"); |
||||
|
System.out.println(answer1); |
||||
|
String answer2 = assistant.chat("我是谁"); |
||||
|
System.out.println(answer2); |
||||
|
} |
||||
|
|
||||
|
|
||||
|
// 聊天记忆的简单实现
|
||||
|
@Test |
||||
|
public void testChatMemory2() { |
||||
|
//第一轮对话
|
||||
|
UserMessage userMessage1 = UserMessage.userMessage("我是环环"); |
||||
|
ChatResponse chatResponse1 = qwenChatModel.chat(userMessage1); |
||||
|
AiMessage aiMessage1 = chatResponse1.aiMessage(); |
||||
|
//输出大语言模型的回复
|
||||
|
System.out.println(aiMessage1.text()); |
||||
|
//第二轮对话
|
||||
|
UserMessage userMessage2 = UserMessage.userMessage("你知道我是谁吗"); |
||||
|
ChatResponse chatResponse2 = qwenChatModel.chat(Arrays.asList(userMessage1, |
||||
|
aiMessage1, userMessage2)); |
||||
|
AiMessage aiMessage2 = chatResponse2.aiMessage(); |
||||
|
//输出大语言模型的回复
|
||||
|
System.out.println(aiMessage2.text()); |
||||
|
} |
||||
|
|
||||
|
// 使用ChatMemory实现聊天记忆
|
||||
|
@Test |
||||
|
public void testChatMemory3() { |
||||
|
//创建chatMemory
|
||||
|
MessageWindowChatMemory chatMemory = MessageWindowChatMemory.withMaxMessages(10); |
||||
|
//创建AIService
|
||||
|
Assistant assistant = AiServices |
||||
|
.builder(Assistant.class) |
||||
|
.chatLanguageModel(qwenChatModel) |
||||
|
.chatMemory(chatMemory) |
||||
|
.build(); |
||||
|
//调用service的接口
|
||||
|
String answer1 = assistant.chat("我是环环"); |
||||
|
System.out.println(answer1); |
||||
|
String answer2 = assistant.chat("我是谁"); |
||||
|
System.out.println(answer2); |
||||
|
} |
||||
|
|
||||
|
|
||||
|
@Autowired |
||||
|
private MemoryChatAssistant memoryChatAssistant; |
||||
|
@Test |
||||
|
public void testChatMemory4() { |
||||
|
String answer1 = memoryChatAssistant.chat("我是环环"); |
||||
|
System.out.println(answer1); |
||||
|
String answer2 = memoryChatAssistant.chat("我是谁"); |
||||
|
System.out.println(answer2); |
||||
|
} |
||||
|
|
||||
|
@Autowired |
||||
|
private SeparateChatAssistant separateChatAssistant; |
||||
|
@Test |
||||
|
public void testChatMemory5() { |
||||
|
String answer1 = separateChatAssistant.chat(1,"我是环环"); |
||||
|
System.out.println(answer1); |
||||
|
String answer2 = separateChatAssistant.chat(1,"我是谁"); |
||||
|
System.out.println(answer2); |
||||
|
String answer3 = separateChatAssistant.chat(2,"我是谁"); |
||||
|
System.out.println(answer3); |
||||
|
} |
||||
|
|
||||
|
//系统提示词@SystemMessage
|
||||
|
@Test |
||||
|
public void testSystemMessage() { |
||||
|
String answer = separateChatAssistant.chat(3,"今天几号"); |
||||
|
System.out.println(answer); |
||||
|
} |
||||
|
|
||||
|
@Test |
||||
|
public void testUserMessage() { |
||||
|
String answer = memoryChatAssistant.chat("我是环环"); |
||||
|
System.out.println(answer); |
||||
|
} |
||||
|
@Test |
||||
|
public void testV() { |
||||
|
String answer1 = separateChatAssistant.chat2(1, "我是环环"); |
||||
|
System.out.println(answer1); |
||||
|
String answer2 = separateChatAssistant.chat2(1, "我是谁"); |
||||
|
System.out.println(answer2); |
||||
|
} |
||||
|
@Test |
||||
|
public void testUserInfo() { |
||||
|
String answer = separateChatAssistant.chat3(1, "我是谁，我多大了", "翠花", 18); |
||||
|
System.out.println(answer); |
||||
|
} |
||||
|
|
||||
|
} |
||||
@ -0,0 +1,19 @@ |
|||||
|
package cn.ai.springaigu; |
||||
|
|
||||
|
import cn.ai.springaigu.asistant.SeparateChatAssistant; |
||||
|
import org.junit.jupiter.api.Test; |
||||
|
import org.springframework.beans.factory.annotation.Autowired; |
||||
|
import org.springframework.boot.test.context.SpringBootTest; |
||||
|
|
||||
|
@SpringBootTest |
||||
|
public class ToolsTest { |
||||
|
@Autowired |
||||
|
private SeparateChatAssistant separateChatAssistant; |
||||
|
@Test |
||||
|
public void testCalculatorTools() { |
||||
|
String answer = separateChatAssistant.chat(1, "1+2等于几，475695037565的平方根是多 少？"); |
||||
|
//答案：3，689706.4865
|
||||
|
System.out.println(answer); |
||||
|
|
||||
|
} |
||||
|
} |
||||
Loading…
Reference in new issue